我已经尝试实现 Nguyen Widrow 算法(如下),它似乎运行正常,但我有一些后续问题:
这看起来像一个正确的实现吗?
Nguyen Widrow 初始化是否适用于任何网络拓扑/大小?(即5层AutoEncoder)
Nguyen Widrow 初始化对任何输入范围都有效吗?(0/1、-1/+1 等)
Nguyen Widrow 初始化对任何激活函数都有效吗?(即 Logistic、Tanh、Linear)
下面的代码假设网络已经被随机化为 -1/+1 :
' Calculate the number of hidden neurons
Dim HiddenNeuronsCount As Integer = Me.TotalNeuronsCount - (Me.InputsCount - Me.OutputsCount)
' Calculate the Beta value for all hidden layers
Dim Beta As Double = (0.7 * Math.Pow(HiddenNeuronsCount, (1.0 / Me.InputsCount)))
' Loop through each layer in neural network, skipping input layer
For i As Integer = 1 To Layers.GetUpperBound(0)
' Loop through each neuron in layer
For j As Integer = 0 To Layers(i).Neurons.GetUpperBound(0)
Dim InputsNorm As Double = 0
' Loop through each weight in neuron inputs, add weight value to InputsNorm
For k As Integer = 0 To Layers(i).Neurons(j).ConnectionWeights.GetUpperBound(0)
InputsNorm += Layers(i).Neurons(j).ConnectionWeights(k) * Layers(i).Neurons(j).ConnectionWeights(k)
Next
' Add bias value to InputsNorm
InputsNorm += Layers(i).Neurons(j).Bias * Layers(i).Neurons(j).Bias
' Finalize euclidean norm calculation
InputsNorm = Math.Sqrt(InputsNorm)
' Loop through each weight in neuron inputs, scale the weight based on euclidean norm and beta
For k As Integer = 0 To Layers(i).Neurons(j).ConnectionWeights.GetUpperBound(0)
Layers(i).Neurons(j).ConnectionWeights(k) = (Beta * Layers(i).Neurons(j).ConnectionWeights(k)) / InputsNorm
Next
' Scale the bias based on euclidean norm and beta
Layers(i).Neurons(j).Bias = (Beta * Layers(i).Neurons(j).Bias) / InputsNorm
Next
Next